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Exploratory Search upon
 Semantically Described
   Web Data Sources
              Marco Brambilla
               Politecnico di Milano

                                           marco.brambilla@polimi.it
                                           marcobrambi

     SSW workshop @ VLDB 2012, Istanbul, Turkey
Outline




• Context
• Search Services Specification
   •   Description
   •   Semantic Annotation

• Exploratory Search
• Design patterns
• Demo
• Outlook
Context


Web is a huge, heterogeneous data source:
 Structured, unstructured and semi-structured data
 Known problems of trust, reputation, consistency


User needs to solve real-life problems, not to find a web site
Context


Google? Well, yes… an “interesting” system
Context




User needs to solve real-life problems, not to find a web site
 Web queries get increasingly complex and specialized
 Exploratory search
 From document search to object search


Search as a service
 Viability of systems based upon search service orchestration
What are search services?

• APIs over Web data sources
    •   Structured data
    •   Domain-specific
• Wrapping of information utility sites
How can we use them?

• Applying complex queries (also with “joins”)
“… search for upcoming concerts close to an attractive location (like a
   beach, lake, mountain, natural park, and so on), considering also availability
   of good, close-by hotels …”
Background: semantic multi-domain search




 “… expand the search to get information about available restaurants near
  the candidate concert locations, news associated to the event and possible
  options to combine further events …”
Liquid Query: Query Submission



 Example Scenario 1: Trip planner for events




    Concert                            Hotels
    query conditions                   query conditions
Liquid Query: Query Execution
Liquid Query: alternative visualizations
and domain-independent platform

Example Scenario 2: Scientific Publication search
Problem 1: Service specification




• No service description per se
• Focused on search
   •   Ranking aware

• Description
   •   Bottom-up
   •   Based on the service interface

• Annotation
   •   Relying on an external reference knowledge base
SDF and SAF

Service Description (SDF) vs. Service Annotation (SAF)
Example of SDF instances
The registration of services
Bottom-up approach from the service signatures
Registration process fully specified and implemented
• Starts from SI details (name, type of service, etc.) and SI field
  details, i.e. name, data type and I/O directionality
• the name and I/O fields of the SI are scanned with NLP and
  Semantic techniques in order to identify the most suitable
  Domain Diagram items to represent them
• The expert user's intervention is required to provide a
  feedback concerning system-hypothesized mappings
• When all mappings have been validated, a newly created
  Access Pattern and its corresponding Service Mart are
  committed

See demo video at: http://search-computing.it/registration_demo
Example of resulting service mart




• A set of predefined combinations of services, to be
  reused for specific cases
Problem 2: Reduce flexibility

Maximum flexibility over huge amounts of search services is not always the
  best solution
 People want straightforward paths and want to be quick
 Commercial implementations are likely to be on fixed sets of domains and
  fixed exploration directions
Design Patterns




• A set of blueprint combinations of services, to be
  reused for different cases
• Very much like UML design patterns or datamart
  patterns
Design Patterns – some examples




• Sequence
Design Patterns – some examples



• Star
Design Patterns – integrated examples



• Join
Design Patterns – integrated examples



• Join
Exploration implementation



• Not just a matter of data sources
• Also: data visualization, user interface specification,
  usability, ..




See demo videos at:
http://demo.search-computing.net/night_planner_demo/seco/seco.html
http://demo.search-computing.net/new_job_demo/seco/seco.html
Problem 3 - Outlook

When dealing with real-life problems, people do not trust the web
  completely
 Want to go back to discussion with people
 Expect insights, opinions, reassurance




 Exploratory search must be blended with social-network based
  recommendations and inputs
Social Search: increasing quality in search



• From exploratory search to friends and experts feedback
           Initial
           query

                                  Exploration
                 Exploratory         step           Human
                   Search                           Search
                  System                            System


                                      Exploration
                                      step

                 System API                         Social API


                     Database /                         Crowd /
                     IR index                           Community
Example: Find your job (social invitation)




Selected data items
can be transferred
to the crowd question
Find your job (response submission)
Conclusions and future work


Well, I’ve shown everything..
See our papers at WWW 2010 (Liquid Query) and WWW 2012
 (CrowdSearcher)


Future work
• More experiments (e.g., vs. sociality of users, vs. crowds, …)
• Not only search: active integration of web structured data and
  social sensors




Some ads
• Search Computing book series (Springer LNCS)
• Workshop Very Large Data Search at VLDB
• VLDB Journal special issue (deadline Sept 2012)
Thanks!

Questions?


                Marco Brambilla
             marco.brambilla@polimi.it
             marcobrambi

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Exploratory Search upon Semantically Described Web Data Sources: Service registration and methodology. At vldb2012

  • 1. Exploratory Search upon Semantically Described Web Data Sources Marco Brambilla Politecnico di Milano marco.brambilla@polimi.it marcobrambi SSW workshop @ VLDB 2012, Istanbul, Turkey
  • 2. Outline • Context • Search Services Specification • Description • Semantic Annotation • Exploratory Search • Design patterns • Demo • Outlook
  • 3. Context Web is a huge, heterogeneous data source:  Structured, unstructured and semi-structured data  Known problems of trust, reputation, consistency User needs to solve real-life problems, not to find a web site
  • 4. Context Google? Well, yes… an “interesting” system
  • 5. Context User needs to solve real-life problems, not to find a web site  Web queries get increasingly complex and specialized  Exploratory search  From document search to object search Search as a service  Viability of systems based upon search service orchestration
  • 6. What are search services? • APIs over Web data sources • Structured data • Domain-specific • Wrapping of information utility sites
  • 7. How can we use them? • Applying complex queries (also with “joins”) “… search for upcoming concerts close to an attractive location (like a beach, lake, mountain, natural park, and so on), considering also availability of good, close-by hotels …”
  • 8. Background: semantic multi-domain search “… expand the search to get information about available restaurants near the candidate concert locations, news associated to the event and possible options to combine further events …”
  • 9. Liquid Query: Query Submission Example Scenario 1: Trip planner for events Concert Hotels query conditions query conditions
  • 10. Liquid Query: Query Execution
  • 11. Liquid Query: alternative visualizations and domain-independent platform Example Scenario 2: Scientific Publication search
  • 12. Problem 1: Service specification • No service description per se • Focused on search • Ranking aware • Description • Bottom-up • Based on the service interface • Annotation • Relying on an external reference knowledge base
  • 13. SDF and SAF Service Description (SDF) vs. Service Annotation (SAF)
  • 14. Example of SDF instances
  • 15. The registration of services Bottom-up approach from the service signatures Registration process fully specified and implemented • Starts from SI details (name, type of service, etc.) and SI field details, i.e. name, data type and I/O directionality • the name and I/O fields of the SI are scanned with NLP and Semantic techniques in order to identify the most suitable Domain Diagram items to represent them • The expert user's intervention is required to provide a feedback concerning system-hypothesized mappings • When all mappings have been validated, a newly created Access Pattern and its corresponding Service Mart are committed See demo video at: http://search-computing.it/registration_demo
  • 16. Example of resulting service mart • A set of predefined combinations of services, to be reused for specific cases
  • 17. Problem 2: Reduce flexibility Maximum flexibility over huge amounts of search services is not always the best solution  People want straightforward paths and want to be quick  Commercial implementations are likely to be on fixed sets of domains and fixed exploration directions
  • 18. Design Patterns • A set of blueprint combinations of services, to be reused for different cases • Very much like UML design patterns or datamart patterns
  • 19. Design Patterns – some examples • Sequence
  • 20. Design Patterns – some examples • Star
  • 21. Design Patterns – integrated examples • Join
  • 22. Design Patterns – integrated examples • Join
  • 23. Exploration implementation • Not just a matter of data sources • Also: data visualization, user interface specification, usability, .. See demo videos at: http://demo.search-computing.net/night_planner_demo/seco/seco.html http://demo.search-computing.net/new_job_demo/seco/seco.html
  • 24. Problem 3 - Outlook When dealing with real-life problems, people do not trust the web completely  Want to go back to discussion with people  Expect insights, opinions, reassurance  Exploratory search must be blended with social-network based recommendations and inputs
  • 25. Social Search: increasing quality in search • From exploratory search to friends and experts feedback Initial query Exploration Exploratory step Human Search Search System System Exploration step System API Social API Database / Crowd / IR index Community
  • 26. Example: Find your job (social invitation) Selected data items can be transferred to the crowd question
  • 27. Find your job (response submission)
  • 28. Conclusions and future work Well, I’ve shown everything.. See our papers at WWW 2010 (Liquid Query) and WWW 2012 (CrowdSearcher) Future work • More experiments (e.g., vs. sociality of users, vs. crowds, …) • Not only search: active integration of web structured data and social sensors Some ads • Search Computing book series (Springer LNCS) • Workshop Very Large Data Search at VLDB • VLDB Journal special issue (deadline Sept 2012)
  • 29. Thanks! Questions? Marco Brambilla marco.brambilla@polimi.it marcobrambi